In this section, we compare the performance between one-stage and two-stage 360 monocular depth estimation approach. Besides the vanilla one-stage approach, we also add several one-stage approaches with techniques that utilizing multi-tasking or additional refinement module to improve depth quality. For multi-tasking, there are methods that combines normal and boundary estimation [1]}, or semantic segmentation and layout prediction [2]}, with depth estimation task.
In this experiment, we train a multi-task depth network with a shared weight encoder and two decoders for both depth and normal maps prediction.
For refinement, we append another CoordNet to the first CoordNet as refinement network, which takes the original RGB and the coarse depth estimation as input and outputs a refined depth map.
The results is shown in Table REF . Adding a refinement network boosts the performance by 7.8% (Abs Rel). Multi-tasking improves the performance by 15.9% (Abs Rel). However, multi-tasking usually requires additional training labels which is not suitable for many cases where only ground truth depth is available. Our PanoDepth is able to outperform the vanilla CoordNet by 18.5%, which has the best performance. Besides, since PanoDepth is model-agnostic, we can even add multi-tasking to the coarse depth estimation network to further improve the performance of the full pipeline.
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